ML with multi-domain data such as images, videos, point clouds, texts,
speeches, audios, graphs is the emerging trend. It is straightforward
to apply GL to a single data domain for representation learning.
Non-image-domain applications of GL can be found in some exploratory
work - videos [1]}, point clouds [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, texts [10]}, [11]}, graphs
[12]}, [13]} and knowledge graphs
[14]}, [15]}. Generally, we can concatenate
representation vectors learned from each domain into “hybrid"
representation vectors for multi-domain input samples. The main
challenge is that the dimension of hybrid representation vectors is
extremely high. The training data will be too sparse in the
representation space to yield meaningful learning results. We need some
mechanism to zoom into a lower dimensional space. To this end, we may
leverage co-occurrence (e.g., joint or conditional probabilities),
priors, constraints.
